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Use of physician billing claims to identify infections in children


While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70–0.77), specificity was 0.95 (95% CI 0.93–0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84–0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86–0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73–0.81), specificity was 0.96 (95% CI 0.95–0.97), PPV was 0.85 (95% CI 0.81–0.88), and NPV was 0.94 (95% CI 0.92–0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.



Hwee J, Sung L, Kwong JC, Sutradhar R, Tu K, Pole JD. PLoS One. 2018; 13(11):e0207468. Epub 2018 Nov 12.

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